「agm」の検索結果

Think and work like an expert Emergency Medicine Researcher. Use when a task calls for Emergency Medicine Researcher judgment. ED trial design expert for pragmatic and cluster RCTs, time-zero and immortal-time bias in acute cohorts, NIHSS/SOFA/qSOFA and ESI/CTAS triage, NEDS/NHAMCS registries, and CONSORT/SPIRIT reporting with Hawthorne and selection-bias failure modes.

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Think and work like an expert Algebraist. Use when a task calls for Algebraist judgment. Reasons from carriers, operations, and morphisms through isomorphism theorems, universal properties, exact-sequence and homological tools (Ext, Tor, snake lemma), and computational systems like GAP, Magma, SageMath, and Lean while treating silently smuggled hypotheses (commutativity, units, Noetherian, algebraically closed base), characteristic-p false friends, and noncanonical isomorphisms as first-class failure modes.

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Maps GWAS-implicated loci to candidate effector (causal) genes by integrating variant-to-gene (V2G) features via Open Targets L2G (Mountjoy 2021), MAGMA gene-based association (de Leeuw 2015), FUMA SNP2GENE, cS2G combined SNP-to-gene scores (Gazal 2022), Polygenic Priority Scores (PoPS, Weeks 2023), FLAMES, INQUISIT, DEPICT, and enhancer-gene predictors (ABC, ENCODE-rE2G). Use when narrowing a GWAS lead locus to a candidate causal gene, picking between proximity, eQTL-based, and similarity-based prioritizers, integrating multi-evidence streams (fine-mapping, colocalization, ABC enhancer-gene, distance, chromatin), reconciling discordant L2G vs PoPS calls, prioritizing tissue-specific eQTL evidence, or triangulating across at least three independent lines of evidence for a publication-grade effector-gene nomination.

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Normalize read-depth copy-ratio profiles and segment them into copy-number regions using circular binary segmentation (CBS, DNAcopy), hidden Markov models, HaarSeg, and fused-lasso methods. Covers GC-content, mappability, and replication-timing (wave-artifact) bias correction, panel-of-normals/PCA denoising, diploid-baseline centering, and algorithm selection by sequencing depth and event size. Use when choosing a segmentation algorithm, correcting depth bias, diagnosing oversegmentation or a mis-centered baseline, tuning CBS or HMM parameters, or understanding why a downstream CNV caller produced fragmented or shifted segments.

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Analyzes CUT&RUN (Skene Henikoff 2017) and CUT&Tag (Kaya-Okur 2019) chromatin profiling data. Handles SEACR vs MACS2 peak calling (with the btaf375 2025 benchmark guidance), pA-MNase vs pA-Tn5 vs pAG-Tn5 chimera differences, E. coli spike-in carryover normalization, IgG-only control logic (no input), characteristic fragment-size signatures (25-75 bp for CUT&Tag), and lower depth requirements (5M reads typical vs 25M for ChIP). Use when calling peaks from CUT&RUN/CUT&Tag, scaling by E. coli spike-in carryover, choosing SEACR norm mode, or comparing CUT&RUN/Tag results to traditional ChIP.

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Assesses ChIP-seq quality across antibody specificity, fragmentation, enrichment, replicate concordance, and library complexity. Computes FRiP, NSC/RSC (phantompeakqualtools), library complexity (NRF/PBC1/PBC2), deepTools plotFingerprint (JS distance, AUC, synthetic JS), ChIPQC, IDR with ENCODE Nself/Nt rules, and detects hyper-ChIPable artifacts. Use when validating an antibody, diagnosing failed peak calls, deciding whether to proceed with downstream analysis, grading against ENCODE thresholds, or auditing replicate concordance.

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Select colormaps and qualitative palettes for scientific figures using perceptual-uniformity, color-vision-deficiency safety, and luminance-monotonicity criteria. Covers Crameri scientific colormaps, viridis/cividis/magma, Okabe-Ito categorical, ColorBrewer, and the rainbow/jet critique. Use when choosing palettes for heatmaps, scatter, networks, or any encoding where color carries quantitative or categorical meaning.

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Comprehensive quality control for CLIP-seq libraries (eCLIP, iCLIP, iCLIP2, PAR-CLIP) covering library complexity (preseq), FRiP, IDR replicate reproducibility, read-distribution metagene, SMInput vs IgG control rationale, rRNA / snoRNA contamination, fragment-length distribution, and ENCODE-compliance thresholds. Use when assessing whether a CLIP library passed, deciding lenient vs stringent peak thresholds, comparing replicates with IDR rescue and self-consistency ratios, or distinguishing failed IP from over-amplified library.

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ATAC-seq library quality control -- TSS enrichment, FRiP, fragment-size periodicity, library complexity (NRF/PBC1/PBC2), mitochondrial fraction, and ENCODE 4 thresholds. Use when assessing whether an ATAC-seq library passes ENCODE acceptance criteria, diagnosing transposition artefacts, comparing Omni-ATAC vs standard prep quality, or selecting which replicates to drop before peak calling.

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A vibrant claymation-meets-data interface for Clay.com (GTM data-orchestration platform). Anchors on white canvas with dark-navy primary CTAs, custom rounded display type, and saturated single-color feature cards — hot pink, deep teal, lavender, peach, ochre — that punctuate long-scroll explainer pages. Brand voltage comes from 3D-rendered claymation illustrations (mountains, characters, mascots) used as full-bleed hero artifacts and the bright multi-color card surfaces showing product UI fragments.

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Author a HyperFrames slideshow — a presentation, pitch deck, or interactive deck with discrete slides, fragment reveals, branching, hotspot navigation, and built-in presenter mode with speaker notes; also converts an existing page into a deck. Output is a navigable deck, not a rendered MP4. If the user didn't explicitly ask for a slideshow, confirm before authoring. Unclear → /hyperframes.

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A clean, calendar-software-first interface anchored on white canvas with black primary CTAs and custom Cal Sans display typography. The system reads as friendly modern SaaS — generous whitespace, soft-rounded cards (~12px), product UI fragments shown directly inside cards, and a dark navy footer that visually closes long-scroll pages. Brand voltage comes from the Cal Sans display headline (a custom geometric face) and from product UI artifacts shown in-card rather than from accent colors.

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Assess adoption-lifecycle stage, plan the chasm crossing, and build a beachhead strategy using Geoffrey Moore's Crossing the Chasm framework during PRD v1.0 Market Adoption. Triggers on requests to assess adoption stage, plan beachhead, cross the chasm, scale from early adopters, or when user asks "are we in the chasm?", "crossing the chasm", "beachhead strategy", "Moore", "whole product", "pragmatist buyers", "from early adopters to early majority". Outputs ADO-STAGE-*, ADO-BEACHHEAD-*, ADO-WHOLE-*, ADO-REF-* entries.

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Writing maintainable code - readability principles, SOLID patterns applied pragmatically, and the judgment to know when rules should bendUse when "code quality, clean code, readability, naming, SOLID, refactor, code review, best practices, maintainable, how should I structure, clean-code, solid, readability, maintainability, code-review, naming, functions, principles" mentioned.

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Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragmented with inconsistent terminology. We use the CoALA cognitive architecture framework: semantic memory (facts), episodic memory (experiences), and procedural memory (how-to knowledge). Use when "agent memory, long-term memory, memory systems, remember across sessions, memory retrieval, episodic memory, semantic memory, vector store, rag, langmem, memgpt, conversation history, memory, vector-store, rag, retrieval, embedding, episodic, semantic, procedural, langmem, memgpt, pinecone, qdrant, chromadb" mentioned.

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Google and Meta paid media account structure evaluation. Audits campaign/ad set architecture against conversion volume minimums, budget thresholds, and targeting overlap. Identifies over-segmentation, under-segmentation, budget fragmentation, and structural anti-patterns blocking algorithmic learning. Provides consolidation roadmaps with migration plans. Use when inheriting accounts, quarterly health checks, or before scaling budgets.

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Strips AI-generation "smell" from prose before it ships (READMEs, docs, release notes, PR descriptions, posts, emails). Counts measurable tells first (keyword saturation, tricolons and anaphora, the "not X, it's Y" reflex, fragment-for-emphasis, bolded thesis lines, listicle-ization, signpost phrases, filler vocabulary, em-dashes, engineered-quotable closers), then applies a "doing work or reflex?" test to each instance so genuine voice survives. Use when asked to de-slop text or make it sound human, when text reads as machine-written, and as the final pass on prose the agent drafted.

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加密链上交易 (加密 / 链上交易 (Crypto & On-chain Trading) — 以加密资产为标的、强调链上活动的主动交易认知操作系统：从「看叙事/选赛道 → 找标的 → 链上尽调与风控 → 选场所(DEX/CEX/链上合约) → 建仓与仓位管理 → 监控链上流向与情绪 → 退出与复盘」的完整决策链。覆盖 (a) 第一性张力 — **链上 on-chain (DEX 现货/AMM、钱包追踪 wallet tracking、资金流向 flow、MEV、memecoin、链上衍生品 Hyperliquid/GMX、自托管 self-custody, 'not your keys not your coins') ⇄ 链下 CEX (中心化交易所现货/合约 Binance/OKX/Bybit、订单簿、托管、出入金法币)**; **合约/杠杆/衍生品 (perps 永续、资金费率 funding、清算 liquidation、爆仓、做多做空、高频/择时) ⇄ 现货/价值/长持 (spot、HODL、定投 DCA、基本面/估值、'time in market > timing the market')**; **价值/基本面派 (协议收入、TVL、代币经济学 tokenomics、真实使用、估值模型) ⇄ meme/叙事/情绪派 (memecoin、narrative rotation 叙事轮动、attention is the asset、纯博弈/PvP、Ponzinomics)**; **链上数据/量化/客观 (on-chain analytics, Nansen/Arkham/Dune、聪明钱 smart money、链上指标、系统化/机械化) ⇄ 直觉/盘感/叙事阅读 (narrative、KOL alpha、社群情绪、tape reading)**; **自托管/去中心化/抗审查 (DeFi、自托管钱包、抗审查、'be your own bank') ⇄ 便利/合规/中心化 (CEX、合规出入金、KYC、托管、ETF)**; **主动交易/择时/高换手 (trading、波段、合约、撸毛/空投 airdrop farming、套利 arbitrage) ⇄ 被动配置/低换手 (配置 BTC/ETH、ETF、长期叙事 beta)**; (b) 核心工作流 / pipeline (最标准、最易蒸高质量 + CLI 化) — 通用链: 叙事/赛道判断 (宏观周期/减半/流动性/板块轮动) → 标的发现 (链上扫链/聪明钱跟单/新币/叙事映射) → 尽调 due diligence (代币经济学/解锁 unlock/团队/合约审计/honeypot 检测/流动性与持仓分布) → 风控前置 (仓位 sizing/最大回撤/止损/相关性) → 场所与执行 (CEX vs DEX、滑点/MEV/gas、限价 vs 市价、TWAP) → 持仓监控 (链上流向/巨鲸/资金费率/解锁日历/情绪指标) → 退出与复盘 (止盈分批/再平衡/记录交易日志); 子流派 SOP: 链上 degen 打新与 memecoin 流程、合约交易者的资金费率与清算地图、空投/撸毛 farming、套利与 MEV、被动配置与定投; (c) 工具栈 — 链上分析 (Nansen 聪明钱/Arkham 实体标注/Dune 自建仪表盘/Dexscreener & DEX Screener 新币/GMGN & Photon & BullX memecoin 终端/Etherscan & Solscan 区块浏览器/DeBank 钱包总览/Token Terminal 协议基本面/DefiLlama TVL/Glassnode & CryptoQuant 链上指标)、交易场所 (CEX: Binance/OKX/Bybit/Coinbase; 链上现货 DEX: Uniswap/Jupiter/Raydium/Aerodrome; 链上衍生品: Hyperliquid/GMX/dYdX)、钱包与安全 (MetaMask/Rabby/Phantom 热钱包、Ledger/Trezor 硬件冷钱包、Revoke.cash 授权管理、honeypot/合约扫描)、行情与图表 (TradingView、CoinGecko/CoinMarketCap)、信息与聚合 (Twitter/X、Telegram、DeFiLlama、RootData/ICO 日历、解锁日历 Token Unlocks)、自动化 (交易机器人、链上 bot/sniper、Telegram trading bot); (d) 知识正典 — 注意加密交易 canon 偏少且新: 通用交易/风控经典 (Edwin Lefèvre 'Reminiscences of a Stock Operator'、Van Tharp 'Trade Your Way to Financial Freedom' 仓位管理、Mark Douglas 'Trading in the Zone' 交易心理、Michael Mauboussin、Nassim Taleb 'Fooled by Randomness/Antifragile')、加密原生 (Bitcoin whitepaper 中本聪、'The Bitcoin Standard' Saifedean、Vitalik 博客与 Ethereum whitepaper、'DeFi and the Future of Finance'、Lyn Alden 宏观、a16z crypto 文章、Messari/Delphi/Bankless 研报)、链上分析方法 (Glassnode/CryptoQuant 教程、Nansen/Arkham/Dune 文档、Dune Analytics SQL)、交易方法论散见于 podcast/Substack/Twitter thread 而非书; (e) figures/流派 — 价值/基本面派 (Vitalik Buterin 以太坊、Hasu/Su Zhu(争议)、Arthur Hayes BitMEX 创始人/宏观+衍生品、Raoul Pal、Lyn Alden 宏观、Cobie/Jordan Fish UpOnly podcast、DeFi 研究 Bankless Ryan Adams/David Hoffman); 链上分析派 (Nansen Alex Svanevik、Arkham、Adam Cochran、Ansem(blknoiz06) 链上交易、Murad memecoin supercycle、链上侦探 ZachXBT 反诈); 合约/衍生品派 (Arthur Hayes、GCR(GiganticRebirth) 传奇合约、Pentoshi、CryptoCred 技术分析、Tyler Durden); meme/叙事派 (Ansem、Murad、Cobie、各 Solana memecoin KOL); 宏观/周期派 (Raoul Pal、Lyn Alden、PlanB stock-to-flow(争议)、Benjamin Cowen 数据/周期); 量化/系统化 (各匿名量化、Hyperliquid 生态); 中文圈 (链上分析/合约 KOL、币圈大 V、播客如 '无人之境'/'Web3 佛学院'、研究机构如 Foresight/PANews/律动 BlockBeats); 反诈/安全 (ZachXBT、SlowMist 慢雾、go+ 安全); (f) 行业话术/黑话 — 现货 spot / 合约 perps 永续 / 杠杆 leverage / 多空 long-short / 爆仓 liquidation 清算 / 资金费率 funding rate / 开多开空 / 止损 SL 止盈 TP / 仓位 position sizing / 满仓梭哈 ape in / 梭 / FOMO / FUD / 钻石手 diamond hands / 纸手 paper hands / 韭菜 / 镰刀 / 庄 / 出货 / 拉盘砸盘 / 插针 wick / 针 / 上车下车 / 埋伏 / 潜伏 / 链上 on-chain / 钱包 wallet / 地址 / 聪明钱 smart money / 巨鲸 whale / 跟单 copy-trade / 老鼠仓 / 貔貅盘 honeypot / 貔貅 / rug 跑路 rug-pull / 归零 / 土狗 (memecoin/小币) / 金狗 (暴涨百倍) / meme 币 / 叙事 narrative / 赛道 / 板块轮动 / 撸毛 撸空投 airdrop / 女巫 sybil / 交互 / 解锁 unlock / 砸盘解锁 / 代币经济学 tomomics / TVL / APY APR / 无常损失 IL / 滑点 slippage / MEV / 三明治 sandwich / gas / 抢跑 frontrun / 貔貅检测 / 授权 approve / 撤销授权 revoke / 私钥 助记词 seed phrase / 冷钱包 热钱包 / 自托管 / 减半 halving / 牛熊 / 山寨季 altseason / 比特币市占率 BTC.D / 山寨 alt / 主流币 / DeFi 暑期 / GM / WAGMI / NGMI / 归零归零 / PvP / Ponzi 庞氏; (g) 争议/批

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Applies the reasoning of Pieter Abbeel, robotics and reinforcement learning expert, UC Berkeley professor, and co-founder of Covariant. Use this skill whenever you are designing AI systems, tackling Sim2Real transfer, deploying machine learning in the physical world, or evaluating reinforcement learning architectures. Trigger this skill for questions about domain randomization, reward design, bootstrapping real-world AI, robotics hardware assumptions, or shifting from hard-coded rules to data-driven deep learning. It helps ground theoretical AI in physical embodiment and pragmatic deployment.

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Applies the pragmatic, financially-grounded reasoning of Marian Ilitch (co-founder of Little Caesars Pizza). Use this skill whenever the user is discussing business partnerships, balancing visionary marketing with financial due diligence, franchise unit economics, family business dynamics, or hands-on financial management. Trigger this when users face trade-offs between rapid growth and profitability, need to enforce unit-level accountability, or are paralyzed by the fear of making mistakes. Apply her principles to ground lofty ideas in tactile, common-sense financial reality.

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Applies the reasoning style of Daphne Koller (machine learning pioneer, co-founder of Coursera, founder and CEO of Insitro). Use this skill whenever you encounter problems involving AI and machine learning in biology, drug discovery, interdisciplinary collaboration, data generation vs. data mining, or transitioning from academia to industry. Trigger this skill when advising on career trade-offs, building cross-functional teams (especially bridging engineers and domain experts), designing data pipelines, evaluating causality vs. correlation, or applying AI to physical systems ('where bits meet atoms'). Channel her focus on fit-for-purpose data, pragmatism, and disproportionate leverage.

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Applies the reasoning, architectural principles, and AI philosophy of Christopher Manning (natural language processing expert, Stanford University, director of Stanford AI Lab). Use this skill whenever you are discussing natural language processing, LLM architecture, AI research strategy, cognitive science, or the evolution of machine learning. Trigger this skill for questions about AGI timelines, academic vs. industry research trade-offs, linguistic structure in neural networks, modularity in AI design, or evaluating true intelligence versus mere memorization. Channel his pragmatic focus on domain science, adaptability, and competing on ideas rather than raw compute.

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Applies the reasoning, frameworks, and mental models of Fei-Fei Li, computer vision pioneer, ImageNet creator, and co-director of Stanford HAI. Use this skill whenever Claude encounters topics related to AI ethics, human-centered AI, spatial intelligence, embodied AI, robotics, AI governance, diversity in tech, or the societal impacts of AI. Trigger this skill when users face decisions about AI product design (augment vs. replace), dataset formulation, navigating AI regulation, or choosing audacious research directions. Channel her pragmatic optimism and focus on spatial, physical grounding over pure language models.

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Build GraphRAG retrieval pipelines on Neo4j using the neo4j-graphrag Python package (v1.22.0+). Covers retriever selection (VectorRetriever, HybridRetriever, VectorCypherRetriever, HybridCypherRetriever, Text2CypherRetriever, ToolsRetriever), external vector DB retrievers (Weaviate, Pinecone, Qdrant), retrieval_query Cypher fragments, query_params, filters, GraphRAG pipeline wiring (GraphRAG + LLM + prompt), all LLM providers (OpenAI, Anthropic, Gemini/VertexAI, Bedrock, Cohere, Mistral, Ollama), embedder setup, index creation, token usage tracking, Cypher 25 SEARCH clause, and LangChain/LlamaIndex integration. Does NOT handle KG construction — use neo4j-document-import-skill. Does NOT handle plain vector search — use neo4j-vector-index-skill. Does NOT handle GDS analytics — use neo4j-gds-skill. Does NOT handle agent memory — use neo4j-agent-memory-skill.

日本語の概要は準備中です。原文の説明を表示しています。